{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom Analysis of Training Results\n",
    "\n",
    "Notebook demonstrates two methods for plotting training results.  First method uses Ludwig's visualization api.  Second method illustrates converting Ludwig training statistics into a pandas dataframe and plotting data with seaborn package.\n",
    "\n",
    "This notebook is dependent on running the multiple model training example beforehand.  To run the mulitple model training example, enter this command:\n",
    "``` \n",
    "python multiple_model_training.py\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "from ludwig.utils.data_utils import load_json\n",
    "from ludwig.visualize import learning_curves\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os.path\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Annotated Learning Curves Using Ludwig Visualization API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# retrieve training statistics\n",
    "list_of_stats = []\n",
    "list_of_models = []\n",
    "\n",
    "for model in ['model1', 'model2']:\n",
    "    experiment_model_dir = './results/multiple_experiment_' + model \n",
    "    train_stats = load_json(os.path.join(experiment_model_dir,'training_statistics.json'))\n",
    "    list_of_stats.append(train_stats)\n",
    "    list_of_models.append(model)\n",
    "    \n",
    "\n",
    "# generating learning curves from training\n",
    "learning_curves(list_of_stats, 'Survived',\n",
    "                model_names=list_of_models,\n",
    "                output_directory='./visualizations',\n",
    "                file_format='png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Annotated Learning Curves Using seaborn package\n",
    "\n",
    "### Helper function to collect training statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to generate pandas data frame from training statistcs\n",
    "# Parameter:\n",
    "#   experiment_model_dir: directory containing the training statistics for a specific model training experiment\n",
    "#\n",
    "# Returns: pandas dataframe containing the performance metric and loss\n",
    "#\n",
    "\n",
    "def extract_training_stats(experiment_model_dir):\n",
    "    list_of_splits = ['training', 'validation', 'test']\n",
    "    list_of_df = []\n",
    "    for split in list_of_splits:\n",
    "        train_stats = load_json(os.path.join(experiment_model_dir,'training_statistics.json'))\n",
    "        df = pd.DataFrame(train_stats[split]['combined'])\n",
    "        df.columns = [split + '_' + c for c in df.columns]\n",
    "        list_of_df.append(df)\n",
    "        \n",
    "    df = pd.concat(list_of_df, axis=1)\n",
    "    df['epoch'] = df.index + 1\n",
    "        \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve training results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "model1 = extract_training_stats('./results/multiple_experiment_model1')\n",
    "model1.name = 'model1'\n",
    "model2 = extract_training_stats('./results/multiple_experiment_model2')\n",
    "model2.name = 'model2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>training_loss</th>\n",
       "      <th>validation_loss</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.646716</td>\n",
       "      <td>7.069627</td>\n",
       "      <td>7.541213</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.495552</td>\n",
       "      <td>6.909096</td>\n",
       "      <td>7.370474</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.315707</td>\n",
       "      <td>6.718051</td>\n",
       "      <td>7.167351</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.135415</td>\n",
       "      <td>6.526596</td>\n",
       "      <td>6.963694</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.954975</td>\n",
       "      <td>6.334948</td>\n",
       "      <td>6.759850</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   training_loss  validation_loss  test_loss  epoch\n",
       "0       6.646716         7.069627   7.541213      1\n",
       "1       6.495552         6.909096   7.370474      2\n",
       "2       6.315707         6.718051   7.167351      3\n",
       "3       6.135415         6.526596   6.963694      4\n",
       "4       5.954975         6.334948   6.759850      5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper function to generate plot ready data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create pandas dataframe suitable for plotting learning curves\n",
    "# Parameters\n",
    "#   train_df_list: list of pandas datatframe containing training statistics\n",
    "#\n",
    "# Returns: plot ready pandas dataframe\n",
    "\n",
    "def create_plot_ready_data(list_of_train_stats_df):\n",
    "    # holding ready for plot ready data\n",
    "    plot_ready_list = []\n",
    "    \n",
    "    # consolidate the multiple training statistics dataframes\n",
    "    for df in list_of_train_stats_df:\n",
    "        for col in ['training', 'validation']:\n",
    "            df2 = df[['epoch', col + '_loss']].copy()\n",
    "            df2.columns = ['epoch', 'loss']\n",
    "            df2['type'] = col\n",
    "            df2['model'] = df.name\n",
    "            plot_ready_list.append(df2)\n",
    "\n",
    "    return pd.concat(plot_ready_list, axis=0, ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot learning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create plot ready data\n",
    "learning_curves = create_plot_ready_data([model1, model2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Learning Curves')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot learning curves for the different models\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "sns.set_style(style='dark')\n",
    "ax = sns.lineplot(x='epoch', y='loss',\n",
    "             style='type',\n",
    "             hue='model',\n",
    "             data=learning_curves)\n",
    "ax.set_title('Learning Curves', fontdict={'fontsize': 16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('./visualizations/custom_learning_curve.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
